Collaborative and distributed business processes increasingly span multiple information systems, organizational units, and independent stakeholders. Analyzing such processes often requires diverse process mining techniques implemented across heterogeneous tools and platforms. However, analysts face significant challenges in discovering suitable methods and accessing them through interoperable environments. Existing research has primarily addressed either method discoverability through surveys and classifications or method accessibility through service-oriented implementations, with limited support for integrating both capabilities within a unified framework.This paper proposes an AI-powered service-oriented framework that enhances the discoverability and accessibility of process mining methods in collaborative and distributed environments. The framework combines a graph-based representation of process mining methods, a unified REST-based service architecture, and a Large Language Model (LLM)-based matching mechanism that translates natural language requirements into executable service requests. Through this approach, analysts can identify and invoke process mining capabilities through a common interaction interface.A proof-of-concept implementation and user study were conducted to evaluate the framework. Results indicate that the approach reduces the effort required to discover and access process mining methods while improving user experience. The study contributes a unified mechanism that bridges method discoverability and service accessibility, supporting interoperable and user-centric process mining ecosystems.

Process mining techniques allow organizations to discover, monitor and improve their as-is processes by analyzing the process execution data, aka event data, recorded by their information systems. A recurrent task in process mining is querying. Querying allows users to get insights into specific executions of their processes and to retrieve relevant data. Existing process querying techniques require end users to be knowledgeable of the query language and the database schema. However, a key success factor for process analysis is to make querying accessible to business experts who may be inexperienced in database querying. This paper addresses this challenge by proposing a natural language interface (NLI) for querying event data. The interface allows users to formulate their questions in natural language and to automatically translate the questions into a structured query that can be executed over a database. We use graph based storage techniques, namely labeled property graphs, which allow to explicitly model event data relationships. As an executable query language, we use the Cypher language which is widely used for querying property graphs. The approach has been implemented and evaluated using a publicly available event log.

With the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

Social networking sites (SNSs) facilitate the sharing of ideas and information through different types of feedback including publishing posts, leaving comments and other type of reactions. However, some comments or feedback on SNSs are inconsiderate and offensive, and sometimes this type of feedback has a very negative effect on a target user. The phenomenon known as flaming goes hand-in-hand with this type of posting that can trigger almost instantly on SNSs. Most popular users such as celebrities, politicians and news media are the major victims of the flaming behaviors and so detecting these types of events will be useful and appreciated. Flaming event can be monitored and identified by analyzing negative comments received on a post. Thus, our main objective of this study is to identify a way to detect flaming events in SNS using a sentiment prediction method. We use a deep Neural Network (NN) model that can identity sentiments of variable length sentences and classifies the sentiment of SNSs content (both comments and posts) to discover flaming events. Our deep NN model uses Word 2Vec and FastText word embedding methods as its training to explore which method is the most appropriate. The labeled dataset for training the deep NN is generated using an enhanced lexicon based approach. Our deep NN model classifies the sentiment of a sentence into five classes: Very Positive, Positive, Neutral, Negative and Very Negative. To detect flaming incidents, we focus only on the comments classified into the Negative and Very Negative classes. As a use-case, we try to explore the flaming phenomena in the news media domain and therefore we focused on news items posted by three popular news media on Facebook (BBCNews, CNN and FoxNews) to train and test the model. The experimental results show that flaming events can be detected with our proposed approach, and we explored main characteristics that trigger a flaming event and topics discussed in the flaming posts.

The rising popularity of social media has radically changed the way news content is propagated, including interactive attempts with new dimensions. To date, traditional news media such as newspapers, television and radio have already adapted their activities to the online news media by utilizing social media, blogs, websites etc. This paper provides some insight into the social media presence of worldwide popular news media outlets. Despite the fact that these large news media propagate content via social media environments to a large extent and very little is known about the news item producers, providers and consumers in the news media community in social media. To better understand these interactions, this work aims to analyze news items in two large social media, Twitter and Facebook. Towards that end, we collected all published posts on Twitter and Facebook from 48 news media to perform descriptive and predictive analyses using the dataset of 152K tweets and 80K Facebook posts. We explored a set of news media that originate content by themselves in social media, those who distribute their news items to other news media and those who consume news content from other news media and/or share replicas. We propose a predictive model to increase news media popularity among readers based on the number of posts, number of followers and number of interactions performed within the news media community. The results manifested that, news media should disperse their own content and they should publish first in social media in order to become a popular news media and receive more attractions to their news items from news readers.

Chapter 4 Big Data Integration in Cloud Environments: Requirements, Solutions and Challenges Rami Sellami, Search for more papers by this authorBruno Defude, Search for more papers by this author Rami Sellami, Search for more papers by this authorBruno Defude, Search for more papers by this author Book Editor(s):Olivier Pivert, Search for more papers by this author First published: 06 August 2018 https://doi.org/10.1002/9781119528227.ch4Citations: 1 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Summary This chapter focuses on the existing solutions of the state of the art supporting Big Data integration in cloud environments. Optimization is the 'holy grail' of database management and, in the context of Big Data integration, it is clearly a major challenge. Choosing one or multiple data stores based on data requirements is a very important step before integrating heterogeneous data stores and deploying and running applications in a Cloud environment. Ruiz-Alvarez proposes an automatic approach to selecting a cloud storage service according to the application requirements and the storage services capabilities. Object NoSQL Datastore Mapper (ONDM) is a framework aiming to facilitate persistent object storage and retrieval in NoSQL data stores. The chapter presents some substantial work proposing different unified data models to manage heterogeneous data integration. It analyzes how global queries are processed. Citing Literature NoSQL Data Models: Trends and Challenges, Volume 1 RelatedInformation

The production of huge amount of data and the emergence of cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: relational and NoSQL (i.e., document, graph, key-value, and column) data stores. Interacting with heterogeneous data models via different APIs and query languages imposes challenging tasks to the developers of multiple data stores applications. Indeed, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with single data store application, and therefore requires extra implementation efforts. In this paper we propose a mediation based component to optimize and execute complex queries over multiple data stores in Cloud environments. This component is referred to as virtual data store (VDS). The key ingredients of our solution are (1) a simple global schema describing the different data sources and their relationships, (2) a cost model to evaluate the cost of the operations, (3) an inter data stores parallelism execution model, and (4) a dynamic programming based approach to generate optimal execution plan. Quantitative and qualitative experiments are conducted to validate our approach.

2016 Intl IEEE Conferences on Advanced and Trusted Computing(ATC), Toulouse, France, July 18-21, 2016

Service composition is a major advance service-oriented computing brings to enable the development of distributed applications. However, the distributed nature of services hampers their composition with data heterogeneity problems. In this paper, we address these problems with a decentralised mediation-as-a-service architecture that solves data inconsistencies occurring during the composition of business services. As an extension to our previous work that focused on data interpretation problems, we present in this paper a solution to solve data inconsistencies at the syntactic, structural and semantic levels. We show how syntactic, structural and semantic mediation techniques can be combined, and how semantic mediation provides useful information that helps structural and syntactic mediation. We demonstrate how our architecture enables decentralised publication and discovery of mediation services. We motivate our work with a concrete scenario and validate our proposal with experiments.

The production of huge amount of data and the emergence of cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: traditional data types, documents, graph data from social networks, simple key-value data, etc. Interacting with heterogeneous data models via different APIs, and multiple data store applications imposes challenging tasks to their developers. Indeed, programmers have to be familiar with different APIs. In addition, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with mono-data store application, and therefore requires extra implementation efforts. Moreover, developers need to master and deal with the complex processes of cloud discovery, and application deployment and execution. In this paper we propose an integrated set of models, algorithms and tools aiming at alleviating developers task for developing, deploying and migrating multiple data stores applications in cloud environments. Our approach focuses mainly on three points. First, we provide a unifying data model used by applications developers to interact with heterogeneous relational and NoSQL data stores. Based on that, they express queries using OPEN-PaaS-DataBase API (ODBAPI), a unique REST API allowing programmers to write their applications code independently of the target data stores. Second, we propose virtual data stores, which act as a mediator and interact with integrated data stores wrapped by ODBAPI. This run-time component supports the execution of single and complex queries over heterogeneous data stores. Finally, we present a declarative approach that enables to lighten the burden of the tedious and non-standard tasks of (1) discovering relevant cloud environment and (2) deploying applications on them while letting developers to simply focus on specifying their storage and computing requirements. A prototype of the proposed solution has been developed and is currently used to implement use cases from the OpenPaaS project.

The production of huge amount of data and the emergence of cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: traditional data types, documents, graph data from social networks, simple key-value data, etc. Interacting with heterogeneous data models via different APIs, multidata store applications imposes challenging tasks to their developers. Indeed, programmers have to be familiar with different APIs. In addition, developers need to master and deal with the complex processes of cloud discovery, and application deployment and execution. Moreover, the execution of join queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with mono-data store application, and therefore requires extra implementation effort. In this paper we propose a declarative approach enabling to lighten the burden of the tedious and non-standard tasks of discovering relevant cloud environment and deploying applications on them while letting developers to simply focus on specifying their storage and computing requirements. A prototype of the proposed solution has been developed and is currently used to implement use cases from the OpenPaaS project.

Provides an abstract for each of the keynote presentations and may include a brief professional biography of each

Avec l'intention de la conception par la reutilisation, les modeles de processus configurables fournissent un moyen de modeliser la variabilite dans les modeles de processus de reference qui doivent etre configures en fonction des besoins specifiques. Puisque ces processus ont tendance a etre larges et complexes, leur configuration devient une tâche fastidieuse. Les utilisateurs finaux ont besoin d'aide pour selectionner les choix de configuration souhaitables. Dans cet article, nous proposons une approche automatisee d'aide a la configuration des modeles de processus configurables. Notre objectif est d'apprendre de l'experience acquise grâce a la modelisation et a la configuration precedentes des processus afin d'extraire des directives de configuration qui montrent l'interrelation entre les choix de configuration frequemment selectionnes. L'approche est mise en oeuvre comme une extension de Signavio et testee en utilisant le referentiel de modele de processus de SAP

Recently, Sensor/Actuator Networks have become an emergent technology for various application areas such as security and surveillance applications, traffic control, logistics, energy control in public and private buildings, etc. Designing and constructing new applications using these technologies remain, however, a challenging task. Indeed, finding the relevant sensors and actuators, and combining them in a proper way in order to achieve a specific goal is not an easy task and requires several skills from different stakeholders. Moreover, sensor environments are inherently highly dynamic. Furthermore, current applications are in general tightly coupled to the underlying infrastructure which hampers their reuse and flexibility to changes. In this paper, we present a process-oriented and service-based approach for supporting the development of adaptive sensor-based applications. Our approach decouples application logic from its implementation. A design-time model is first specified, as a flow of activities, which is then deployed in a particular environment. Decoupling the application logic from its implementation enables, on one hand, to foster the reuse at the application level and, on the other hand, to adapt the same application to different environments and situations. We propose also an activity recommendation technique to provide assistance to application designers by recommending to them activities that have been used in a similar compositional context. Our approach has been prototyped using service standards such as BPMN within the context of the VITRO European project and validated by several use cases.

As business requirements become increasingly challenging in today’s fast changing environments, cross-organizational collaboration gains more and more attention for a successful business process design. Since many organizations may work on similar processes with some variations, configurable reference models have been proposed as a key aspect for a flexible process design. However, the complexity introduced by such models remains an open issue. The designer ends up with one model that integrates a family of process variants making the process design and update a complex task. In this work, we propose to assist the designer with configurable process fragments. However, instead of building the configurable process fragment from existing process models, we propose to use event logs as input. Such recorded executions capture the real behavior of processes which cannot be derived from their designed models. Then, using these logs we derive guidelines that direct the configuration of the resulted fragment. Our approach has been implemented as a plugin in the ProM framework and tested using a collection of event logs.

Cloud computing has recently emerged as a new computing paradigm enabling on-demand and scalable provision of resources, platforms and software as services. In order to satisfy different storage requirements, cloud applications usually need to access and interact with different relational and NoSQL data stores having heterogeneous APIs. This APIs heterogeneity induces two main problems. First it ties cloud applications to specific data stores hampering therefore their migration. Second, it requires developers to be familiar with different APIs. In this paper, we propose a generic resources model defining the different concept used in each type of data store. These resources are managed by ODBAPI a streamlined and a unified REST API enabling to execute CRUD operations on different NoSQL and relational databases. ODBAPI decouples cloud applications from data stores alleviating therefore their migration. Moreover it relieves developers task by removing the burden of managing different APIs.

In today’s fast changing business environment, the business process design becomes a complex, time-consuming and error-prone task. Thus, many approaches have been proposed to facilitate the business process design phase. Some solutions such as defining and using process reference models or searching similar processes to a working one can facilitate the designer’s work. However, recommending the whole process model can make the designer confused, especially in case of large-size business processes. In this paper, we propose an approach that assists the design phase with configurable process fragments. Concretely, we present an algorithm for merging process fragments around a particular activity to construct a consolidated fragment. This consolidated fragment is presented as a configurable sub-process that can be configured by the business process designer to create business process variants. The approach has been implemented as an extension of the Signavio Process Editor platform which is a web-based modelling tool for business processes. It has been also tested against a large collection of business process models taken from different domains. Experimental results show that our approach produces concise and efficient configurable fragments.

This article focuses on data aggregation in vehicular ad hoc networks (VANETs). In such networks, data produced by sensors or crowdsourcers are exchanged between vehicles in order to warn or inform drivers when an event occurs (e.g., an accident, a traffic congestion, a parking space released, a vehicle with non-functioning brake lights, etc.). In the following, we propose to generate spatio-temporal aggregates containing these data in order to keep a summary of past events. We therefore use Flajolet-Martin sketches. Our goal is then to exploit these aggregates to better assist the drivers. These aggregates may indeed produce additional knowledge that may be useful when no event has been recently transmitted by surrounding vehicles or when some knowledge about the global demand may improve the decision that need to be taken at the vehicle level. To prove the effectiveness of our approach, an extensive experimental evaluation has been performed considering vehicles looking for an available parking space, that proves the interest of our proposal. The experimentations indeed show that the use of our aggregation structure significantly reduces the time needed to actually find a parking space. It also increases the percentage of vehicles finding such a resource in a bounded time in congested situations.

Privacy is among the key challenges to data integration in many sectors, including healthcare, e-government, etc. The PAIRSE project aims at providing a flexible, looselycoupled and privacy-preserving data integration system in P2P environments. The project exploits recent Web standards and technologies such asWeb services and ontologies to export data from autonomous data providers as reusable services, and proposes the use of service composition as a viable solution to answer data integration needs on the fly. The project proposed new composition algorithms and service/composition execution models that preserve privacy of data manipulated by services and compositions. The proposed integration system was demonstrated at EDBT 2013 and VLDB 2011.